A novel approach for data stream maximal frequent itemsets mining
by Chong-Huan Xu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 10, No. 3, 2016

Abstract: This paper proposes a novel algorithm AMMFI based on self-adjusting and orderly compound policy to solve the problems of existing algorithms for mining maximal frequent itemsets in a data stream. The proposed algorithm processes the data stream based on sliding window technique and scans data stream fragments single-pass to obtain and store frequent itemsets in frequent itemsets list. It then constructs a self-adjusting and orderly FP-tree, dynamically adjusts the tree structure with the insertion of itemsets, uses mixed subset pruning method to reduce the search space, and merges nodes with the same min_sup in identical branch. Finally, orderly compound FP-tree is generated and it avoids superset checking in the process of mining maximal frequent itemsets. Detailed simulation analysis demonstrates that the presented algorithm is of high efficiency of space and time and is more stable.

Online publication date: Fri, 24-Jun-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com